To understand how massive galaxies are influenced by their surroundings, we present new Atacama Large Millimeter/submillimeter Array and Northern Extended Millimeter Array observations as part of A Multiwavelength Study of Enormous Lyα Nebula (ELAN) Environments (AMUSE 2). These observations target submillimeter sources discovered in single-dish

We investigate the relationships between the cool circumgalactic medium (CGM), traced by Ca II absorption lines, and galaxy properties at z < 0.4 using ∼900,000 galaxy–quasar pairs within 200 kpc from the Year 1 data of the Dark Energy Spectroscopic Instrument (DESI). This large data set enables us to obtain composite spectra with sensitivity

Seyfert galaxies are emerging as a promising source class of high-energy neutrinos. The Seyfert galaxies NGC 4151 and NGC 1068 have respectively come up as the most promising counterparts of a 3σ and of a 4.2σ neutrino excesses detected by IceCube in the TeV energy range. Constraining the very high energy (VHE) emission associated with the neutrino

In this second paper on our variability survey of central stars of planetary nebulae (CSPNe) using the Zwicky Transient Facility (ZTF), we report 11 long-timescale variables with variability timescales ranging from months to years. We also present preliminary analyses based on spectroscopic and/or photometric follow-up observations for six of them

The Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) galaxy and quasar clustering data represents a significant expansion of data from Data Release 1 (DR1), providing improved statistical precision in baryon acoustic oscillation (BAO) constraints across multiple tracers, including bright galaxies, luminous red galaxies, emission

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to